Social network analysis in innovation research: using a mixed methods approach to analyze social innovations
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ORIGINAL ARTICLE
Social network analysis in innovation research: using a mixed methods approach to analyze social innovations Nina Kolleck
Received: 29 July 2013 / Accepted: 24 September 2013 # The Author(s) 2013. This article is published with open access at Springerlink.com
Abstract The importance of social networks for innovation diffusion and processes of social change is widely recognized in many areas of practice and scientific disciplines. Social networks have the potential to influence learning processes, provide opportunities for problem-solving, and establish new ideas. Thus, they can foster synergy effects, bring together key resources such as know-how of participating actors, and promote innovation diffusion. There is wide agreement regarding the usefulness of empirical methods of Social Network Analysis (SNA) for innovation and futures research. Even so, studies that show the chances of implementing SNA in these fields are still missing. This contribution addresses the research gap by exploring the opportunities of a mixed methods SNA approach for innovation research. It introduces empirical results of the author’s own quantitative and qualitative investigations that concentrate on five different innovation networks in the field of Education for Sustainable Development. Keywords Social network analysis . Social innovations . Education for sustainable development . Egocentric network maps . Governance
Introduction Scholars interested in innovation processes and futures research have often stressed the importance of social networks. I thank the editors and two anonymous reviewers for their constructive comments, which helped me to improve the article. The article is based on results of a study I conducted at the Freie Universität Berlin. I would also like to thank Gerhard de Haan for useful information and for supporting my research. N. Kolleck (*) Universität Heidelberg & Hertie School of Governance, Friedrichstraße 180, 10117 Berlin, Germany e-mail: [email protected]
Social networks are seen as an important factor in how ideas, norms, and innovations are realized. Social network research understands individuals within their social context, acknowledging the influence of relationships with others on one’s behavior. Hence, social networks can promote innovation processes and expand opportunities for learning. Despite the consensus regarding the value of social network approaches, there is a lack of empirical investigations in innovation and futures studies that use Social Network Analysis (SNA). In most cases, the scientific literature uses the concept of social networks metaphorically, ignoring the chances presented by SNA methods. At the same time, conventional empirical research in innovation and futures studies often disregards relational information. Hence, analyses of statistical data on structural and individual levels are treated as separately. Activities that are expected to have impacts on future developments are usually modeled as isolated individual or group behavior, on the one
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